We are seeing further progress in the global green transition as governments and private companies invest in research and development to deliver the innovations needed to advance renewable energy projects. Significant advances in artificial intelligence (AI) and other digital products in recent years have enabled some energy experts to use the technology to enhance energy production, increasing the potential for global geothermal energy output. I think it is possible.
Geothermal energy is the natural thermal energy that exists within the earth. There is enough geothermal energy on Earth to meet the world's energy needs, but accessing this energy can be extremely difficult. Geothermal energy can be harnessed in a variety of ways, one of which is direct use, which has been popular for hundreds of years. This involves using heated water near the surface of the earth, such as hot springs or geysers. No drilling equipment is required for this. That water or steam can also be used to heat buildings close to the water source. In contrast, geothermal energy used to generate electricity is harnessed through drilling. Hydrothermal water reservoirs are found in various locations around the world, just a few miles from the Earth's surface. These can be accessed by drilling and steam can be extracted to spin turbines and power generators to produce electricity.
Drilling for geothermal energy remains unpopular due to technical limitations that mean exploration of potential geothermal fields can be expensive. Additionally, hard-to-access reserves can be very difficult to access using existing drilling techniques. Despite the great potential to harness this abundant renewable energy source, geothermal energy currently contributes less than 1% of U.S. electricity.
Utah-based startup Zanskar has built a machine learning model to assess the best locations to drill for geothermal energy. The company's models analyze extensive data to determine the best locations to drill for geothermal energy, which Zanskar believes will lead to significant reductions in exploration costs in the coming years. This could encourage more companies to invest in the geothermal field, leading to diversification of the green energy field.
“In just the past year and a half, we have discovered more hidden geothermal resources than the industry as a whole has discovered in the past 10 years,” Zanskar CEO Karl Hoyland said. This shows the huge potential of new technology and could encourage more energy companies to invest in this area. Earlier this month, Zanskar announced it had raised $30 million in a Series B funding round led by Ovvious Ventures, valuing it at $115 million. To date, Zanskar has raised $45 million in funding, which will help expand exploration and develop its first power plant. The company plans to work with existing geothermal companies to develop the new site.
There is great enthusiasm for the potential of geothermal energy development worldwide, as geothermal energy has the potential to provide an unlimited renewable resource. However, it is often overlooked as project development costs are currently approximately five times that of wind energy. Geothermal-derived electricity costs about $8.7 million per megawatt. The main reason this price is so high is because drillers often cannot find the right location to access the reservoir. This means that even if you find a hole before you run out of time and money, you may have to drill multiple holes before you are successful.
Zanskar uses vast amounts of data collected from satellites, geological surveys, waves traveling through the ground after earthquakes, and other data points to predict the best spots to drill. The more data available within a region, the more accurate the machine learning program will be. Combining this technology with other innovations, such as advanced drilling techniques, could make geothermal energy more accessible and cheaper.
The National Renewable Energy Laboratory (NREL) is also developing AI and machine learning technologies to enhance renewable energy production. The company has developed a suite of algorithms and tools to improve reservoir characterization, save drilling and optimize geothermal steam field operations. Meanwhile, since 2018, the U.S. Geothermal Technology Office (GTO) has been funding early-stage research and development applications in machine learning to develop new technologies for the exploration and improved operation of geothermal resources.
Rapid advances in AI and machine learning technologies are enhancing renewable energy operations, and further advances in the coming decades are expected to optimize energy operations and further reduce costs. If machine learning algorithms are as accurate as Zanskar and other companies hope, some of the key inhibitors to the development of geothermal energy businesses could become a thing of the past, and worldwide Abundant renewable energy sources will be available.
Written by Felicity Bradstock, Oilprice.com
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